Lead the architecture and development of scalable AI pipelines and APIs, optimize models for performance, and oversee AI testing frameworks.
ABOUT US
Billigence Pty Ltd is a specialist in the delivery of market leading Business Intelligence and CRM solutions. Headquartered in Sydney, Australia and with offices in Prague, London, Frankfurt and Singapore our passion is data and our focus is the delivery of end-to-end solutions via a talented team of skilled professionals.
We are partners with leading edge software platforms including Tableau, Alteryx, Collibra, Snowflake, GCP and Salesforce.
Key Responsibilities
- Architect and develop scalable GenAI pipelines, APIs, and microservices for real-time and batch AI applications using frameworks such as FastAPI, Ray, or LangServe.
- Design robust prompt strategies for instruction-following, reasoning, and multi-turn conversations, with a focus on RAG architectures for personalized, domain-specific use cases.
- Lead embedding model selection and tuning to optimize semantic search and RAG performance.
- Oversee LLM Ops workflows, including model orchestration, evaluation, deployment, rollback strategies, and monitoring in production environments.
- Drive model fine-tuning efforts to customize LLMs for proprietary datasets and regulated industries.
- Establish and govern AI testing frameworks, covering functional testing, regression testing, hallucination detection, safety filters, and output quality assessment.
- Implement enterprise-grade observability, lineage tracking, and CI/CD automation using tools such as MLflow, Databricks, Azure ML, or Vertex AI.
- Lead continuous improvement initiatives based on telemetry, user feedback, and cost-performance trade-offs.
- Demonstrate expertise in Python, with deep proficiency in GenAI frameworks, vector search systems, and MLOps toolchains.
Qualifications
- Minimum 5 years’ experience architecting and deploying scalable AI/ML and GenAI solutions in enterprise environments.
- Deep expertise in machine learning, deep learning, and generative AI technologies, including hands-on experience with frameworks like TensorFlow, PyTorch, and modern LLM orchestration tools.
- Strong familiarity with cloud platforms (AWS, Azure, GCP) and MLOps practices for end-to-end machine learning lifecycle management.
- Demonstrated leadership in managing agile, cross-functional teams and collaborating with stakeholders.
- Significant experience in prompt engineering and prompt design for LLMs and GenAI applications.
- Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field; advanced degrees or certifications (e.g., Azure AI Engineer) are advantageous.
- Experience with personalization, recommendation systems, or conversational AI is highly desirable.
If this sounds like something you are interested in, please apply with your most up-to-date CV and we will be in touch!
Only successful candidates will be contacted.
Top Skills
AWS
Azure
Azure Ml
Databricks
Fastapi
GCP
Langserve
Mlflow
Python
PyTorch
Ray
TensorFlow
Vertex Ai
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